This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: multinomial distribution, LaGrange multipliers, Exact Multinomial Test (EMT), the Pearson statistic, and goodness of fit.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: multinomial distribution, LaGrange multipliers, Exact Multinomial Test (EMT), the Pearson statistic, and goodness of fit.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Wald test, score test, likelihood-ratio test, large sample confidence intervals, and the F distribution.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture provides a review of probability and statistical concepts such as conditional probabilities, Bayes Theorem, sensitivity and specificity, and binomial and poisson distributions.
Includes detailed PowerPoints for 20 lectures for topics including generalized linear models, logistic regression, and random effects models.
This is a graduate level introduction to statistics including topics such as probabilty/sampling distributions, confidence intervals, hypothesis testing, ANOVA, and regression. Perfect for students and teachers wanting to learn/acquire materials for this topic.
This course covers methodology, major software tools and applications in data mining. By introducing principal ideas in statistical learning, the course will help students to understand conceptual underpinnings of methods in data mining. It focuses more on usage of existing software packages (mainly in R) than developing the algorithms by the students. The topics include statistical learning; resampling methods; linear regression; variable selection; regression shrinkage; dimension reduction; non-linear methods; logistic regression, discriminant analysis; nearest-neighbors; decision trees; bagging; boosting; support vector machines; principal components analysis; clustering. Perfect for students and teachers wanting to learn/acquire materials for this topic.
The objective of this course is to learn and apply statistical methods for the analysis of data that have been observed over time. Our challenge in this course is to account for the correlation between measurements that are close in time. Perfect for students and teachers wanting to learn/acquire materials for this topic.
This is a graduate level survey course that stresses the concepts of statistical design and analysis in biomedical research, with special emphasis on clinical trials. Perfect for students and teachers wanting to learn/acquire materials for this topic.
Epidemiology is the study of the distribution and determinants of human disease and health outcomes, and the application of methods to improve human health. This course examines the methods used in epidemiologic research, including the design of epidemiologic studies and the collection and analysis of epidemiological data. Perfect for students and teachers wanting to learn/acquire materials for this topic.
The aim of this course is to cover sampling design and analysis methods that would be useful for research and management in many field. A well designed sampling procedure ensures that we can summarize and analyze data with a minimum of assumptions and complications. Perfect for both students and teachers wanting to learn/acquire materials for this topic.